{"id":"W4367276429","doi":"10.47611/jsrhs.v12i1.4408","title":"Anti-Asian Racism in Canada: The Story of the Numbers","year":2023,"lang":"en","type":"article","venue":"Journal of Student Research","topic":"Global Political and Economic Relations","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Racism; Pandemic; Political science; Criminology; Coronavirus disease 2019 (COVID-19); Public relations; Sociology; Law; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044524,0.00002629081,0.00009586968,0.00006792482,0.0004134898,0.00002474643,0.0005898391,0.00002268001,0.00006802956],"category_scores_gemma":[0.0004919692,0.00001419201,0.0000520457,0.0005003024,0.0002724637,0.0000632595,0.0001032393,0.0005502198,0.00001696726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006824193,"about_ca_system_score_gemma":0.002032659,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5791406,"about_ca_topic_score_gemma":0.8333401,"domain_scores_codex":[0.9979555,0.0006007041,0.0002204402,0.00004315378,0.0008946963,0.0002854859],"domain_scores_gemma":[0.9990924,0.0005264618,0.00007165085,0.00008852311,0.0001397366,0.00008120407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000686047,0.00006126994,0.8306745,0.000007170991,0.00005342808,0.00003425161,0.01969823,0.0006046024,0.00002787708,0.05964176,0.08721539,0.001974608],"study_design_scores_gemma":[0.00008887198,0.000007730288,0.9163316,0.0000165785,0.000002031925,9.402773e-7,0.05767262,0.000006494618,0.000003870515,0.002588634,0.02326519,0.00001545254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9152719,0.00005445713,1.680555e-7,0.04401334,0.000305184,0.0000880876,0.000001718667,8.834713e-7,0.04026426],"genre_scores_gemma":[0.9982787,0.0001247372,0.000001229733,0.00005196674,0.0001148561,0.000001138469,4.293811e-8,0.000001697347,0.001425633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2541994,"threshold_uncertainty_score":0.4236619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1662219863261562,"score_gpt":0.4553178099648018,"score_spread":0.2890958236386456,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}